printer friendly pageSTATS 215: Statistical Models in Biology
Poisson and renewal processes, Markov chains in discrete and continuous time, branching processes, diffusion. Applications to models of nucleotide evolution, recombination, the Wright-Fisher process, coalescence, genetic mapping, sequence analysis. Theoretical material approximately the same as in
STATS 217, but emphasis is on examples drawn from applications in biology, especially genetics. Prerequisite: 116 or equivalent.
Terms: Win
| Units: 3
Instructors:
Holmes, S. (PI)
;
Shang, J. (TA)
STATS 216: Introduction to Statistical Learning
Overview of supervised learning, with a focus on regression and classification methods. Syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis;cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; Some unsupervised learning: principal components and clustering (k-means and hierarchical). Computing is done in R, through tutorial sessions and homework assignments. This math-light course is offered via video segments (MOOC style), and in-class problem solving sessions. Prereqs: Introductory courses in statistics or probability (e.g.,
Stats 60 or
Stats 101), linear algebra (e.g.,
Math 51), and computer programming (e.g.,
CS 105). May not be taken for credit by students with credit in
STATS 202 or
STATS 216V.
Terms: Win
| Units: 3
STATS 217: Introduction to Stochastic Processes I
Discrete and continuous time Markov chains, poisson processes, random walks, branching processes, first passage times, recurrence and transience, stationary distributions. Non-Statistics masters students may want to consider taking
STATS 215 instead. Prerequisite: a post-calculus introductory probability course e.g.
STATS 116
Terms: Win, Sum
| Units: 3
STATS 219: Stochastic Processes (MATH 136)
Introduction to measure theory, Lp spaces and Hilbert spaces. Random variables, expectation, conditional expectation, conditional distribution. Uniform integrability, almost sure and Lp convergence. Stochastic processes: definition, stationarity, sample path continuity. Examples: random walk, Markov chains, Gaussian processes, Poisson processes, Martingales. Construction and basic properties of Brownian motion. Prerequisite:
STATS 116 or
MATH 151 or equivalent. Recommended:
MATH 115 or equivalent.
http://statweb.stanford.edu/~adembo/math-136/
Terms: Win
| Units: 4
STATS 229: Machine Learning (CS 229)
Topics: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, deep learning, model/feature selection, learning theory, ML advice, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: knowledge of basic computer science principles and skills at a level sufficient to write a reasonably non-trivial computer program in Python/NumPy to the equivalency of
CS106A,
CS106B, or
CS106X, familiarity with probability theory to the equivalency of
CS 109,
MATH151, or
STATS 116, and familiarity with multivariable calculus and linear algebra to the equivalency of MATH51 or
CS205.
Terms: Aut, Win, Sum
| Units: 3-4
Instructors:
Avati, A. (PI)
;
Charikar, M. (PI)
;
Fox, E. (PI)
;
Guestrin, C. (PI)
;
Koyejo, S. (PI)
;
Ng, A. (PI)
;
Agarwal, R. (TA)
;
Agarwala, S. (TA)
;
Chang, C. (TA)
;
Chi, R. (TA)
;
Chow, W. (TA)
;
Chu, S. (TA)
;
Damiani, A. (TA)
;
Deng, R. (TA)
;
Desai, R. (TA)
;
Ding, Z. (TA)
;
Dong, K. (TA)
;
Frausto, J. (TA)
;
Jeon, H. (TA)
;
Khandelwal, P. (TA)
;
Kumbong, H. (TA)
;
Schaeffer, R. (TA)
;
So, J. (TA)
;
Wang, A. (TA)
;
Wang, R. (TA)
;
Xiao, Z. (TA)
;
Yang, S. (TA)
;
Zhang, E. (TA)
STATS 242: NeuroTech Training Seminar (NSUR 239)
This is a required course for students in the NeuroTech training program, and is also open to other graduate students interested in learning the skills necessary for neurotechnology careers in academia or industry. Over the academic year, topics will include: emerging research in neurotechnology, communication skills, team science, leadership and management, intellectual property, entrepreneurship and more.
Terms: Aut, Win, Spr
| Units: 1
| Repeatable
9 times
(up to 9 units total)
STATS 250: Mathematical Finance (MATH 238)
Stochastic models of financial markets. Risk neutral pricing for derivatives, hedging strategies and management of risk. Multidimensional portfolio theory and introduction to statistical arbitrage. Prerequisite:
Math 136 or equivalent. NOTE: Undergraduates require instructor permission to enroll. Undergraduates interested in taking the course should contact the instructor for permission, providing information about relevant background such as other courses taken.
Terms: Win
| Units: 3
Instructors:
Papanicolaou, G. (PI)
STATS 251: Clinical Trial Design in the Age of Precision Medicine (BIODS 250)
This course offers an overview of statistical foundation for modern clinical trial design in precision medicine research. Starting from a quick review of traditional clinical development paradigm through Phase I to III clinical trials for medical product approval and Phase IV post-marketing studies for safety evaluation, and challenges in the time and society costs, we will introduce recently developed innovative designs and their statistical methodology across all phases of clinical trials. You expected to learn the statistical considerations for novel phase I-II trial designs, master protocols for umbrella, platform and basket trials, adaptive and enrichment designs including subgroup selections, estimand, surrogate and composite endpoints, integration of real-world evidence and patient-focused medical product development, and meta-analysis of clinical trial endpoints. Prerequisites: Working knowledge of statistics and R.
Terms: Win
| Units: 3
STATS 260B: Workshop in Biostatistics (BIODS 260B)
Applications of data science techniques to current problems in biology, medicine and healthcare. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write a two page critical summary of one of the workshops, with the choice made by the student
Terms: Win
| Units: 1-2
| Repeatable
for credit
STATS 261: Intermediate Biostatistics: Analysis of Discrete Data (BIOMEDIN 233, EPI 261)
Methods for analyzing data from case-control and cross-sectional studies: the 2x2 table, chi-square test, Fisher's exact test, odds ratios, Mantel-Haenzel methods, stratification, tests for matched data, logistic regression, conditional logistic regression. Emphasis is on data analysis in SAS or R. Special topics: cross-fold validation and bootstrap inference.
Terms: Win
| Units: 3
Instructors:
Sainani, K. (PI)
;
Villarreal Rodriguez, M. (TA)
Filter Results: